Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Ideas for Fault Detection Using Relation Discovery
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
Volvo Group Trucks Technology, Göteborg, Sweden.ORCID iD: 0000-0001-8255-1276
2012 (English)In: / [ed] Lars Karlsson and Julien Bidot, Linköping: Linköping University Electronic Press, 2012, 1-6 p.Conference paper, Oral presentation only (Refereed)
Abstract [en]

Predictive maintenance is becoming more and more important in many industries, especially taking into account the increasing focus on offering uptime guarantees to the customers. However, in automotive industry, there is a limitation on the engineering effort and sensor capabilities available for that purpose. Luckily, it has recently become feasible to analyse large amounts of data on-board vehicles in a timely manner. This allows approaches based on data mining and pattern recognition techniques to augment existing, hand crafted algorithms.

Automated deviation detection offers both broader applicability, by virtue of detecting unexpected faults and cross-analysing data from different subsystems, as well as higher sensitivity, due to its ability to take into account specifics of a selected, small set of vehicles used in a particular way under similar conditions.

In a project called Redi2Service we work towards developing methods for autonomous and unsupervised relationship discovery, algorithms for detecting deviations within those relationships (both considering different moments in time, and different vehicles in a fleet), as well as ways to correlate those deviations to known and unknown faults. In this paper we present the type of data we are working with, justify why we believe relationships between signals are a good knowledge representation, and show results of early experiments where supervised learning was used to evaluate discovered relations.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 1-6 p.
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3740 ; 071
National Category
Computer Science
Identifiers
URN: urn:nbn:se:hh:diva-17718OAI: oai:DiVA.org:hh-17718DiVA: diva2:528136
Conference
The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS), 14–15 May 2012, Örebro, Sweden
Projects
Redi2Service
Available from: 2012-05-25 Created: 2012-05-24 Last updated: 2015-02-12Bibliographically approved

Open Access in DiVA

ecp12071001(1257 kB)276 downloads
File information
File name FULLTEXT01.pdfFile size 1257 kBChecksum SHA-512
05cc4fb16ec54ef64a89fc8f3bfacb34419c58fac8fdaaed87c5c64d15b87548be244bdba8cbcdfa5e220b765dd2274b31ef653896c26bb4499bbc235611122c
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Nowaczyk, SławomirByttner, StefanPrytz, Rune
By organisation
Intelligent Systems´ laboratory
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 276 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 336 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf